Performance of Flexible Pavement Using Biaxial Geogrids: A Mechanistic Approach Using IITPAVE Software

Abstract

Flexible pavements under heavy traffic must control fatigue cracking in the bituminous layer and permanent deformation in the subgrade. This study examines whether biaxial geogrid reinforcement can reduce pavement thickness for a design traffic of 175 million standard axles (msa), 9% subgrade CBR and 90% reliability.The biaxial geogrids were considered at the Base/WMM–GSB and GSB–Subgrade interfaces. A conventional section was first selected using IRC:37-2018 and checked in IITPAVE. Two reinforced alternatives were then prepared. In the Layer Coefficient Ratio (LCR) method, geogrid benefit was applied to the granular layer coefficients following the adopted design approach. In the Modulus Improvement Factor (MIF) method, the same numerical factors were applied to resilient modulus for comparison. AASHTO relationships were used to connect resilient modulus and structural layer coefficient, and IITPAVE was used to check tangential tensile strain at the bottom of the bituminous layer and vertical compressive strain at the top of the subgrade. The final unreinforced pavement was 630 mm thick. The LCR section was 480 mm thick, saving 150 mm, while the MIF section was 555 mm thick, saving 75 mm. All three final sections satisfied the adopted fatigue and rutting limits. The study shows that biaxial geogrid can reduce granular material demand, but the calculated saving depends on how reinforcement is represented in design.

Introduction

This study investigates the use of biaxial geogrid reinforcement in flexible pavement to reduce pavement thickness while maintaining adequate fatigue and rutting performance. The analysis follows the IRC:37-2018 mechanistic-empirical design approach, using IITPAVE to evaluate tensile strain at the bottom of the bituminous layer and compressive strain at the top of the subgrade. The study compares an unreinforced pavement with two reinforced alternatives based on the Layer Coefficient Ratio (LCR) and Modulus Improvement Factor (MIF) methods.

A common design condition was adopted for all three sections: 175 msa traffic, 9% subgrade CBR, 90% reliability, 20,000 N wheel load, and 0.56 MPa tyre pressure. The pavement consists of bituminous material, WMM base, GSB sub-base, and subgrade. Biaxial geogrid is considered to improve pavement performance through aggregate interlock, lateral restraint, confinement, and improved load distribution. Reinforcement factors of 1.40 for the base and 1.61 for the sub-base were used as analytical inputs.

The LCR method directly increases the structural coefficients of the granular layers, while the MIF method increases their resilient modulus. Because the relationship between resilient modulus and structural coefficient is logarithmic, the same numerical reinforcement factor produces different structural improvements under the two methods. This distinction is an important finding because LCR and MIF should not be treated as interchangeable design parameters.

The unreinforced pavement has a total thickness of 630 mm, consisting of 180 mm bituminous layer, 250 mm base, and 200 mm sub-base. Its fatigue strain is 120.2 × 10?? and rutting strain is 219.1 × 10??, both within the permissible limits. The LCR-reinforced pavement reduces the total thickness to 480 mm, consisting of 170 mm bituminous, 170 mm base, and 140 mm sub-base. It produces a fatigue strain of 87.15 × 10?? and rutting strain of 257.3 × 10??.

The MIF-reinforced pavement has a total thickness of 555 mm, with 170 mm bituminous, 214 mm base, and 171 mm sub-base. Its fatigue strain is 107.9 × 10??, while rutting strain is 241.5 × 10??. Both reinforced sections satisfy the adopted fatigue and rutting criteria.

The LCR approach provides the greatest thickness reduction, approximately 24%, while the MIF approach provides approximately 12% reduction. However, the LCR section has a higher rutting-strain utilization, demonstrating that reducing granular thickness can improve fatigue performance while moving rutting response closer to its allowable limit. Therefore, both fatigue and rutting must be evaluated during optimization.

The study concludes that biaxial geogrid reinforcement can potentially reduce granular pavement thickness while maintaining acceptable mechanistic performance. For the selected design conditions, the LCR method provides the most economical thickness reduction, whereas the MIF method gives a more moderate reduction. However, the results are analytical and depend strongly on the adopted reinforcement factors, traffic level, subgrade condition, material properties, and IITPAVE assumptions.

Before field implementation, product-specific geogrid properties, tensile strength, aperture characteristics, junction efficiency, installation conditions, drainage, seasonal moisture effects, and construction quality should be verified. Field trials or laboratory testing are also recommended. Thus, the study supports geogrid reinforcement as a promising pavement-thickness optimization technique, but the reported savings should not be generalized to other pavement conditions without project-specific validation.

Conclusion

The study compared conventional and biaxial-geogrid-reinforced flexible pavements for 175 msa traffic, 9% subgrade CBR and 90% reliability. The unreinforced pavement was 630 mm thick and produced fatigue and rutting strains of 120.2 x 10^-6 and 219.1 x 10^-6. The LCR design reduced total thickness to 480 mm, saving 150 mm (about 24%), with strains of 87.15 x 10^-6 and 257.3 x 10^-6. The MIF design was 555 mm thick, saving 75 mm (about 12%), with strains of 107.9 x 10^-6 and 241.5 x 10^-6. All three final sections satisfied the adopted IITPAVE criteria. The larger saving from LCR occurs because LCR acts directly on structural layer coefficient, while MIF acts on resilient modulus before the logarithmic AASHTO conversion. Biaxial geogrid can therefore reduce granular material demand, but the benefit depends on the design representation and should be confirmed with product-specific evidence before field construction. Laboratory or full-scale validation and life-cycle cost assessment are recommended for future work.

References

[1] Indian Roads Congress, IRC:37-2018, Guidelines for the Design of Flexible Pavements, Fourth Revision, New Delhi, India, 2018. [2] Indian Roads Congress, IRC:SP:59-2019, Guidelines for Use of Geosynthetics in Road Pavements and Associated Works, First Revision, New Delhi, India, 2019. [3] American Association of State Highway and Transportation Officials, AASHTO Guide for Design of Pavement Structures, Washington, DC, 1993. [4] IITPAVE, Layered Elastic Analysis Program and Software Documentation, Indian Institute of Technology, pavement analysis software documentation. [5] Ministry of Road Transport and Highways, Specifications for Road and Bridge Works, Fifth Revision, Government of India, 2013. [6] G. N. Goud, S. S. Mouli, B. Umashankar, S. Sireesh and R. M. Madhira, “Design and Sustainability Aspects of Geogrid-Reinforced Flexible Pavements—An Indian Perspective,” Frontiers in Built Environment, vol. 6, art. 71, 2020, doi:10.3389/fbuil.2020.00071.

Copyright

Copyright © 2026 Manchala Rama Lakshmi , Dr. G.V.R. Prasada Raju. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.